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Python Data Transfer from mongodb to redshift using dlt Library

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This guide provides instructions on how to use the open-source Python library, dlt, to load data from MongoDB to Redshift. MongoDB is a developer data platform that simplifies data handling, enabling faster delivery of ideas to market. It's built on a leading modern database that supports JSON-like documents. On the other hand, Redshift is a fully managed, petabyte-scale data warehouse service offered by Amazon. It's capable of scaling from a few hundred gigabytes to over a petabyte. By leveraging dlt, you can efficiently transfer data from MongoDB to Redshift for further analysis and processing. For more information about MongoDB, visit here.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about it here.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read more about it here.
  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, dlt notifies stakeholders, allowing them to take necessary actions. Read more about it here.
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. Read more about it here.
  • Supported File Formats: dlt supports a variety of file formats for Redshift including SQL Insert, jsonl, and parquet. Read more about it here.

Getting started with your pipeline locally​

0. Prerequisites​

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt​

First you need to install the dlt library with the correct extras for Redshift:

pip install "dlt[redshift]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from MongoDB to Redshift. You can run the following commands to create a starting point for loading data from MongoDB to Redshift:

# create a new directory
mkdir mongodb_pipeline
cd mongodb_pipeline
# initialize a new pipeline with your source and destination
dlt init mongodb redshift
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


pymongo>=4.3.3
dlt[redshift]>=0.3.5

You now have the following folder structure in your project:

mongodb_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── mongodb/                   # folder with source specific files
│   └── ...
├── mongodb_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials​

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.mongodb]
connection_url = "connection_url" # please set me up!

[destination.redshift.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 5439
connect_timeout = 15

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the MongoDB source in our docs.
  • Read more about setting up the Redshift destination in our docs.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at mongodb_pipeline.py, as well as a folder mongodb that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


from typing import List

import dlt
from dlt.common import pendulum
from dlt.common.pipeline import LoadInfo
from dlt.common.typing import TDataItems
from dlt.pipeline.pipeline import Pipeline

# As this pipeline can be run as standalone script or as part of the tests, we need to handle the import differently.
try:
    from .mongodb import mongodb, mongodb_collection  # type: ignore
except ImportError:
    from mongodb import mongodb, mongodb_collection


def load_select_collection_db(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongodb source to reflect an entire database schema and load select tables from it.

    This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
    """
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='redshift',
            dataset_name="mongo_select",
        )

    # Configure the source to load a few select collections incrementally
    mflix = mongodb(incremental=dlt.sources.incremental("date")).with_resources(
        "comments"
    )

    # Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
    info = pipeline.run(mflix, write_disposition="merge")

    return info


def load_select_collection_db_items(parallel: bool = False) -> TDataItems:
    """Get the items from a mongo collection in parallel or not and return a list of records"""
    comments = mongodb(
        incremental=dlt.sources.incremental("date"), parallel=parallel
    ).with_resources("comments")
    return list(comments)


def load_select_collection_db_filtered(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongodb source to reflect an entire database schema and load select tables from it.

    This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
    """
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='redshift',
            dataset_name="mongo_select_incremental",
        )

    # Configure the source to load a few select collections incrementally
    movies = mongodb_collection(
        collection="movies",
        incremental=dlt.sources.incremental(
            "lastupdated", initial_value=pendulum.DateTime(2016, 1, 1, 0, 0, 0)
        ),
    )

    # Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
    info = pipeline.run(movies, write_disposition="merge")

    return info


def load_select_collection_hint_db(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongodb source to reflect an entire database schema and load select tables from it.

    This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
    """
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='redshift',
            dataset_name="mongo_select_hint",
        )

    # Load a table incrementally with append write disposition
    # this is good when a table only has new rows inserted, but not updated
    airbnb = mongodb().with_resources("listingsAndReviews")
    airbnb.listingsAndReviews.apply_hints(
        incremental=dlt.sources.incremental("last_scraped")
    )

    info = pipeline.run(airbnb, write_disposition="append")

    return info


def load_entire_database(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongo source to completely load all collection in a database"""
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='redshift',
            dataset_name="mongo_database",
        )

    # By default the mongo source reflects all collections in the database
    source = mongodb()

    # Run the pipeline. For a large db this may take a while
    info = pipeline.run(source, write_disposition="replace")

    return info


if __name__ == "__main__":
    # Credentials for the sample database.
    # Load selected tables with different settings
    print(load_select_collection_db())
    # print(load_select_collection_db_filtered())

    # Load all tables from the database.
    # Warning: The sample database is large
    # print(load_entire_database())

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python mongodb_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline local_mongo info

You can also use streamlit to inspect the contents of your Redshift destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline local_mongo show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with Github Actions: dlt allows you to deploy your pipelines using Github Actions. This is a free CI/CD runner that can be scheduled using a cron schedule expression.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow, particularly with Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions, allowing you to run your pipelines in a serverless environment.
  • Other Deployment Options: There are several other ways you can deploy your dlt pipelines. Check out other deployment options for more information.

The running in production section will teach you about:​

  • Monitoring your pipeline: dlt allows you to monitor your pipeline effectively. It provides useful information on the recently loaded data, such as pipeline and dataset name, destination information, and list of loaded packages. Learn more about it here.
  • Setting up alerts: With dlt, you can set up alerts to be notified of any changes or issues in your pipeline. This feature ensures that you are always up-to-date with the state of your pipeline. Find out how to set it up here.
  • Tracing your pipeline: dlt also offers a tracing feature that provides timing information on extract, normalize, and load steps. It also gives full information from where they were obtained. Learn more about tracing here.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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